一种粗到精分块金字塔预测连续排除算法

提出一种粗到精分块金字塔连续排除算法用于减少块匹配运动估计所需计算量.金字塔可以方便地从当前搜索窗的积分帧构建,连续的排除从金字塔顶层到底层逐级进行,将最小SAD值与金字塔提供的一系列越来越紧的界值相比较,可以跳过不必要的SAD计算.这样,算法在保证获得与全搜索算法相同精度的前提下,计算复杂度大大降低.仿真实验结果验证了本文算法与全搜索算法和几种快速搜索算法相结合的有效性.     

一种用于视频编码的块运动估计算法——块特征匹配预测搜索算法

本文提出了一种块特征匹配预测搜索ＢＦＭＰＳ算法,可以用于视频压缩的一些国际标准,如Ｈ．２６１,Ｈ．２６３,ＭＰＥＧ１,ＭＰＥＧ２,ＨＤＴＶ中,ＢＦＭＰＳ算法充分利用了序列图像的实际矢量与预测矢量之间距离的空间分布特征,中心偏置分布特性和时间上的相关特性,并采用了中止判决准则,可以明显地减少了运动搜索复杂度,ＢＦＭＰＳ算法在匹配运算中采用 简单有交的块特征匹配准则函数,相应的块匹配计算复杂度,数据读     

H.264中块匹配的快速搜索算法研究

在新一代视频压缩编码标准H．264中，块匹配运动估计有着举足轻重的地位，它的性能影响着输出码流的质量。在块匹配中运动估计耗时最多，为了减少运动估计的时间和搜索的次数，常采用快速搜索算法。介绍块匹配运动表示法，详细研究块匹配中的3种快速搜索算法的搜索过程，并对3种算法的性能作比较分析。     

Simplex minimisation for fast long-term memory motion estimation

Three algorithms for fast long-term memory block matching motion estimation are proposed. The algorithms are extensions of the fast single-reference simplex minimisation search (SMS) algorithm. The proposed algorithms represent different degrees of compromise between prediction quality and computational complexity. With a long-term memory of 50 frames the proposed algorithms have a computational complexity comparable to that of single-reference full-search while still maintaining the prediction gain of long-term memory motion estimation     

Fast full search motion estimation algorithm using various matchingscans in video coding

To reduce the amount of computation in a full search (FS) algorithm for fast motion estimation, we propose a novel and fast FS motion estimation algorithm. The computational reduction without any degradation in the predicted image comes from fast elimination of impossible motion vectors. We obtain faster elimination of inappropriate motion vectors using efficient matching units from localization of the complex area in image data. We show three properties in block matching of motion estimation. We suggest two fast matching scan algorithms, one from adaptive matching scan and the other from fixed dithering order. Experimentally, we remove the unnecessary computations by about 30% with our proposed algorithm compared to conventional fast matching scan algorithms     

Adaptive block matching algorithm for video compression

A simple adaptive block matching algorithm for video coding is presented. In the proposed algorithm, a matching criterion called bit-plane matching is proposed to sort out a number of blocks with unlikely directive information to the test block from the search window. An adaptive searching method is developed to further improve the computational effectiveness of the system. In this searching method the search origin, search range and pixel subsampling ratio can be adjusted according to the motion content of the block. Simulation results show that the proposed algorithm achieves MSE performance to the full-search and about 92-95% savings in computation     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

Fast full search motion estimation algorithm using early detection of impossible candidate vectors

To reduce the amount of computations for a full search (FS) algorithm for fast motion estimation, we propose a new and fast FS motion estimation algorithm. The computational reduction of our FS motion estimation algorithm comes from fast elimination of impossible motion vectors. We obtain faster elimination of inappropriate motion vectors using efficient matching units from localization of a complex area in image data. In this paper, we show three properties in block matching of motion estimation. We suggest two fast matching scan algorithms: one from adaptive matching scan and the other from fixed dithering order. Experimentally, we remove the unnecessary computations by about 30% with our proposed algorithm compared with the conventional fast FS algorithms.     

Fast block matching algorithm based on the winner-update strategy

Block matching is a widely used method for stereo vision, visual tracking, and video compression. Many fast algorithms for block matching have been proposed in the past, but most of them do not guarantee that the match found is the globally optimal match in a search range. This paper presents a new fast algorithm based on the winner-update strategy which utilizes an ascending lower bound list of the matching error to determine the temporary winner. Two lower bound lists derived by using partial distance and by using Minkowski's inequality are described. The basic idea of the winner-update strategy is to avoid, at each search position, the costly computation of the matching error when there exists a lower bound larger than the global minimum matching error. The proposed algorithm can significantly speed up the computation of the block matching because (1) computational cost of the lower bound we use is less than that of the matching error itself; (2) an element in the ascending lower bound list will be calculated only when its preceding element has already been smaller than the minimum matching error computed so far; (3) for many search positions, only the first several lower bounds in the list need to be calculated. Our experiments have shown that, when applying to motion vector estimation for several widely-used test videos, 92% to 98% of operations can be saved while still guaranteeing the global optimality. Moreover, the proposed algorithm can be easily modified either to meet the limited time requirement or to provide an ordered list of best candidate matches.     

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.     

Interpolation-Free Fractional-Pixel Motion Estimation Algorithms with Efficient Hardware Implementation

This paper presents interpolation-free fractional-pixel motion estimation (FME) algorithms and efficient hardware prototype of one of the proposed FME algorithms. The proposed algorithms use a mathematical model to approximate the matching error at fractional-pixel locations instead of using the block matching algorithm to evaluate the actual matching error. Hence, no interpolation is required at fractional-pixel locations. The matching error values at integer-pixel locations are used to evaluate the mathematical model coefficients. The performance of the proposed algorithms has been compared with several FME algorithms including the full quarter-pixel search (FQPS) algorithm, which is used as part of the H.264 reference software. The computational cost and the performance analysis show that the proposed algorithms have about 90% less computational complexity than the FQPS algorithm with comparable reconstruction video quality (i.e., approximately 0.2 dB lower reconstruction PSNR values). In addition, a hardware prototype of one of the proposed algorithms is presented. The proposed architecture has been prototyped using the TSMC 0.18 μm CMOS technology. It has maximum clock frequency of 312.5 MHz, at which, the proposed architecture can process more than 70 HDTV 1080p fps. The architecture has only 13,650 gates. The proposed architecture shows superior performance when compared with several FME architectures.     

基于块金字塔的快速块匹配算法

在运动图象编码中，块匹配技术有着非常广泛的应用。在众多的匹配算法中，金字塔算法(blocks sum pyramid algorithm，BSPA)由于速度较快、算法比较简单而得到了广泛的应用。在金字塔算法的基础上，本文提出一种新的基于块金字塔的块匹配算法(NBSPA)。算法采用了预测初始中心，行更新等技术，实验结果表明，新的算法有效地降低了运算量。     

Efficient tree structured motion estimation using successive elimination

In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorithm (SEA) is implemented in tree structured motion estimation with a simple and effective method to determine the initial motion vector, which exploits the strong correlation among the partially overlapped variable-size blocks. With identical performance to a full search algorithm, computations for block matching can be reduced to 1%-20%. Further, the SEA can be improved by incorporating two early termination conditions, then named 'Quick SEA'. Finally, a novel fast motion estimation algorithm, successive elimination diamond search (SEDS), is proposed by efficiently integrating the Quick SEA and a modified diamond search pattern. Simulation results show that the proposed Quick SEA can reduce the computational complexity of block matching by 3-5 times compared to the basic SEA. SEDS further reduces by about one-half the computations of Quick SEA. With similar rate distortion performance, 0.2%-1% block matching distortion is calculated for SEDS with corresponding speed-up factors of 100 to 500 in comparison with the full search algorithm.     

FPGA architecture of the LDPS Motion Estimation for H.264/AVC Video Coding

Motion estimation is a highly computational demanding operation during video compression process and significantly affects the output quality of an encoded sequence. Special hardware architectures are required to achieve real-time compression performance. Many fast search block matching motion estimation (BMME) algorithms have been developed in order to minimize search positions and speed up computation but they do not take into account how they can be effectively implemented by hardware. In this paper, we propose three new hardware architectures of fast search block matching motion estimation algorithm using Line Diamond Parallel Search (LDPS) for H.264/AVC video coding system. These architectures use pipeline and parallel processing techniques and present minimum latency, maximum throughput and full utilization of hardware resources. The VHDL code has been tested and can work at high frequency in a Xilinx Virtex-5 FPGA circuit for the three proposed architectures.     

基于矢量叠加和自适应中止的快速运动估计

提出了一种基于帧矢量叠加和自适应中止技术的快速运动估计方法.该方法在不改变搜索策略的前提下,通过采用帧矢量叠加技术,对运动矢量进行传递,等效扩大了搜索区域,显著减少了相邻帧之间对应像素最小绝对误差值的跳变,运算量减少至21.8%;引入中止判决准则,在匹配运算中,自适应的改变决定匹配次数的阈值,将运算量减少为40.2%.以全搜索块匹配算法为例的实验表明,该算法在保持精度不变的前提下,运算量最终减少到8.72%.     

A Modified Block Matching Motion Estimation Algorithm for the Very Low Bit-Rate video Coding

1IntroductionMohonestimationplaysanimportantroleindigitalvideocompression.Block-matchingtechniquehasbeenadoptedinmanyvideocompressionstandardssuchasH.261,MPEG-l,MPEG-2andH.263.Eventhoughthealgorithmtoestimatethemotionvectorsisnotspecifiedexplicitly.However,despiteitssuccessfulapplications,itproducesarathernoisyinchonfield.Inverylowbit-ratevideocodingapplications,theamountofthesideinformationfortheinchonfieldrepresentsanimPOrtantportionofthetotalbit-rate.Therefore,motionestimatetechniques…     

Novel two-layer motion estimation for video coding

In this paper,we proposed a novel Two-layer Motion Estimation （TME） which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of Motion Estimation （ME） in video coding.A layer is an image which is derived from the reference frame such that the sum of a block of pixels in the reference frame determines the point of a layer.It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame.The proposed TME performs a coarse search on the first layer to identify the small region in which the best candidate block is likely to be positioned and then perform local refined search on the next layer to pick the best candidate block in the located small area.The key feature of TME is its flexibility of mixing with any fast search algorithm.Experimental results on a wide variety of video sequences show that the proposed algorithm has achieved both fast speed and good motion prediction quality when compared to well known as well as the state-of-the-art fast block matching algorithms.     

基于结构信息约束法则耦合匹配模型的图像修复算法

针对当前较多图像修复算法依靠固定大小样本块来搜寻最优匹配块,忽略了样本块的结构信息,使修复图像出现间断现象以及振铃现象等不足,利用样本块与其邻域块的近似度,设计了一种采用结构信息约束法则与匹配模型的图像修复算法。将图像的信息熵特征引入到待修复块的优先权计算过程中,获取优先修复块。通过样本块与其邻域块的近似度构造结构信息度量模型,对样本块的结构信息进行度量,并根据度量值建立结构信息约束法则,实现样本块大小的自适应调整。最后,利用图像的色彩及灰度特征构造匹配模型,利用调整后的样本块大小在已知区域中寻找最优匹配块,从而对待修复块进行修复。实验结果显示,所提算法得到的修复图像具备较好的纹理连续性,不存在信息间断现象,对应的结构相似度较高。     

VLSI Architecture of Block Matching Algorithms for Motion Estimation in High Efficiency Video Coding

High-efficiency video coding (HEVC) is a latest video coding standard and the motion estimation unit is the most important block. The work presents the different types of Matching Criteria for Block-Based Motion Estimation technique in HEVC standard. HEVC requires fast motion estimation algorithms to have better real time performance. The hardware implementation of motion estimation helps to achieve high speed though parallel processing. An improved block matching technique is designed with reduced blocks for HEVC. The proposed method has less execution time where only blocks having motion are compared for prediction computation. The searching time complexity is dependent on the number of blocks that are having motion. The searching time of frame having small motion can be reduced to 80–85% as compared to the traditional full search algorithm. In the paper, sum of absolute difference, mean square error and mean absolute difference are computed to find the best matching algorithm for HEVC. However, SAD has less computational complexity with compare to other matching criteria. The results suggest that proposed motion estimation algorithm has better performance with compare to similar previous work.

     

基于膜计算模型的点集匹配算法

点集匹配是计算机视觉和模式识别领域中的一个经典NP问题。膜计算为自然计算的新分支,旨在从单个细胞或组织及器官等细胞群的结构和功能中抽象出新的计算模型或计算思想。在嵌套结构膜优化算法的基础上,提出了一种新的基于膜计算模型的点集匹配算法,结合点集匹配问题的特点,算法引入了三种新的启发式搜索规则,在一定程度上进一步提高了匹配的正确率。与传统优化算法相比,这种新的方法具有更好的全局搜索能力,因此,能够获得点集匹配问题的较好解。实验结果表明,该方法对点集匹配问题的求解是有效的,具有较高的匹配精度和较好的稳定性。